diff --git a/vllm_ascend/models/qwen2_vl.py b/vllm_ascend/models/qwen2_vl.py
index 7879220..a3b4dc1 100644
--- a/vllm_ascend/models/qwen2_vl.py
+++ b/vllm_ascend/models/qwen2_vl.py
@@ -30,10 +30,10 @@ from vllm.config import VllmConfig
 from vllm.model_executor.layers.activation import QuickGELU
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.models.qwen2_vl import (
-    Qwen2VisionAttention, Qwen2VisionBlock, Qwen2VisionTransformer,
-    Qwen2VLDummyInputsBuilder, Qwen2VLForConditionalGeneration,
-    Qwen2VLMultiModalProcessor, Qwen2VLProcessingInfo,
-    apply_rotary_pos_emb_vision)
+    Qwen2VisionAttention, Qwen2VisionBlock, Qwen2VisionPatchEmbed,
+    Qwen2VisionTransformer, Qwen2VLDummyInputsBuilder,
+    Qwen2VLForConditionalGeneration, Qwen2VLMultiModalProcessor,
+    Qwen2VLProcessingInfo, apply_rotary_pos_emb_vision)
 from vllm.model_executor.models.utils import maybe_prefix
 from vllm.multimodal import MULTIMODAL_REGISTRY
 
@@ -125,6 +125,14 @@ class CustomQwen2VisionBlock(Qwen2VisionBlock):
                                                prefix=f"{prefix}.attn")
 
 
+class CustomQwen2VisionPatchEmbed(Qwen2VisionPatchEmbed):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x.matmul(
+            self.proj.weight.data.view(self.embed_dim, -1).transpose(0, 1))
+        return x
+
+
 class CustomQwen2VisionTransformer(Qwen2VisionTransformer):
 
     def __init__(
@@ -135,6 +143,14 @@ class CustomQwen2VisionTransformer(Qwen2VisionTransformer):
         prefix: str = "",
     ) -> None:
         super().__init__(vision_config, norm_eps, quant_config, prefix)
+
+        self.patch_embed = CustomQwen2VisionPatchEmbed(
+            patch_size=vision_config.patch_size,
+            temporal_patch_size=vision_config.temporal_patch_size,
+            in_channels=vision_config.in_channels,
+            embed_dim=vision_config.embed_dim,
+        )
+
         self.blocks = nn.ModuleList([
             CustomQwen2VisionBlock(dim=self.embed_dim,
                                    num_heads=self.num_heads,